SHF: Small: Software-Driven Hardware Resiliency

SHF：小型：软件驱动的硬件弹性

• 批准号: 1320941
• 负责人: Sarita Adve
• 金额: $ 45万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320941&HistoricalAwards=false
• 关键词: SHF; Small; Software; Driven; Hardware

Moore's law continues to provide abundant devices on chip, but they are increasingly subject to failures from many sources. The hardware reliability problem is expected to be pervasive, affecting markets from embedded systems to high performance computing. There is an urgent need for research to address this problem with extremely low overheads in area, performance, and power (precluding traditional redundancy based solutions). Recently, researchers have proposed a software-driven hardware reliability solution that handles only the device faults that become visible to software and cause anomalous software behavior. This line of work has been quite successful in detecting most faults at extremely low cost. Unfortunately, some hardware faults escape detection by the proposed anomaly monitors, resulting in silent data corruption or SDC. These remaining few SDCs have been the Achilles heel of the software-driven hardware resiliency approach and a hindrance to widespread adoption. The proposed research seeks to overcome this obstacle. The research includes methodological innovations that can determine application sites vulnerable to SDCs within a practical workflow and resiliency solution that uses this information to develop low cost detection and recovery techniques to mitigate the impact of SDCs. It builds on a recent resiliency analysis tool developed by the Principle Investigator's group called Relyzer. The key insight is that instead of trying to determine the outcome of each fault site, Relyzer can seek to determine which application sites will produce equivalent outcomes. This enables pruning a large number of sites and focusing on fault injections for just one site per equivalence class, resulting in significant reduction in resiliency evaluation time. In addition to providing a list of SDC vulnerable instructions, Relyzer also provides a wealth of information on why they are vulnerable. This motivates the use of inexpensive application-specific detectors that exploit this information. However, Relyzer has several limitations in speed, accuracy, and generality, precluding its use in a practical workflow. This research will first develop new techniques to address these limitations and to implement them in a tool. Second, this research will explore systematic techniques to develop practical resiliency solutions that exploit the wealth of fault-propagation information exposed by Relyzer. It will develop systematic low-cost detection and recovery techniques, with quantifiable tradeoffs between resiliency and performance overheads, that can be incorporated in a practical workflow for real applications. If successful, this work will address a key challenge in meeting the expectations of Moore's law performance for a wide variety of societal advances. Besides the research benefits, it will provide a concrete tool for practical full application resiliency analysis and will also train graduate students.

摩尔定律继续提供丰富的片上器件，但它们越来越容易受到多种原因的影响。硬件可靠性问题预计将普遍存在，影响从嵌入式系统到高性能计算的市场。迫切需要研究以极低的面积、性能和功耗开销（排除传统的基于冗余的解决方案）来解决这个问题。最近，研究人员提出了一种软件驱动的硬件可靠性解决方案，该解决方案仅处理软件可见并导致软件行为异常的设备故障。这一系列工作非常成功地以极低的成本检测到大多数故障。不幸的是，一些硬件故障逃避了所提出的异常监视器的检测，导致静默数据损坏或 SDC。剩下的少数 SDC 是软件驱动的硬件弹性方法的致命弱点，也是广泛采用的障碍。拟议的研究旨在克服这一障碍。 该研究包括方法创新，可以确定实际工作流程中易受 SDC 影响的应用程序站点，以及弹性解决方案，该解决方案使用此信息开发低成本检测和恢复技术，以减轻 SDC 的影响。 它建立在原理研究者小组最近开发的名为 Relyzer 的弹性分析工具的基础上。关键的见解是，Relyzer 可以尝试确定哪些应用程序站点将产生相同的结果，而不是尝试确定每个故障站点的结果。这使得能够修剪大量站点并专注于每个等价类仅一个站点的故障注入，从而显着减少弹性评估时间。除了提供 SDC 易受攻击的指令列表之外，Relyzer 还提供了大量有关其易受攻击的原因的信息。这促使人们使用廉价的专用检测器来利用这些信息。然而，Relyzer 在速度、准确性和通用性方面存在一些限制，阻碍了其在实际工作流程中的使用。这项研究将首先开发新技术来解决这些限制并在工具中实现它们。其次，本研究将探索系统技术来开发实用的弹性解决方案，利用 Relyzer 公开的大量故障传播信息。它将开发系统性的低成本检测和恢复技术，并在弹性和性能开销之间进行可量化的权衡，这些技术可以合并到实际应用程序的实际工作流程中。如果成功，这项工作将解决满足摩尔定律对各种社会进步的期望的关键挑战。除了研究优势之外，它还将为实际的全面应用弹性分析提供具体工具，并培训研究生。